What are the data analysis methods?

There are 4 data analysis methods, namely: 1. Trend analysis, which is generally used for long-term tracking of core indicators; 2. Quadrant analysis, which can divide each comparison subject into four groups based on different data. in each quadrant; 3. Comparative analysis, which is divided into horizontal comparison and vertical comparison; 4. Cross-sectional analysis, the main function is to subdivide the data from multiple dimensions.

#The operating environment of this article: windows10 system, thinkpad t480 computer.

The update of science and technology and the rapid development of the Internet are driving the advent of the big data era. Every day, all walks of life are generating an unpredictable amount of data fragments. Only by capturing, managing, processing, and sorting out these huge databases within a reasonable period of time can companies help enterprises obtain the data they want and thus better propose business and management strategies.

Data analysis is more about interpreting data based on business background, refining and summarizing the hidden information behind the data, and discovering valuable content.

Because in this process, the data is objective and the people are in charge. The conclusions drawn by different people from the same data may be different, or even completely opposite, but the conclusion itself is not right or wrong. Therefore, some scientific analysis methods are needed to bridge the gap from objective data to subjective people. Data information is delivered better, more comprehensively and faster.

Common methods of data analysis

1. Trend analysis

When there is a lot of data, and we want to learn from the data faster and more conveniently When discovering data information, you need to use the power of graphics. The so-called power of graphics is to draw it with the help of EXCEl or other drawing tools.

Trend analysis is generally used for long-term tracking of core indicators, such as click-through rate, GMV, and number of active users. Generally, a simple data trend chart is made, but just making a data trend chart is not analysis. It must be like the above, what are the changes in the trend of the data, is there any periodicity, is there an inflection point, and analyze the reasons behind it, regardless of Is it an internal reason or an external reason. The best output from trend analysis is ratios. There are month-on-month, year-on-year, and fixed-base ratios. For example, how much GDP increased in April 2017 compared with March, this is the month-on-month ratio. The month-on-month ratio reflects the recent changing trend, but it has seasonal effects. In order to eliminate seasonal effects, a year-on-year calculation is introduced. For example, the GDP growth rate in April 2017 compared with April 2016 is the year-on-year growth rate. The fixed base ratio is easier to understand, that is, a certain base point is fixed. For example, the data in January 2017 is used as the base point, and the fixed base ratio is the comparison between the data in May 2017 and the data in January 2017.

2. Quadrant analysis

Divide each comparison subject into four quadrants based on different data. If IQ and EQ are divided, they can be divided into two dimensions and four quadrants, and each person has his or her own quadrant. Generally speaking, IQ guarantees a person's lower limit, and EQ increases a person's upper limit.

An example of the quadrant analysis method used in actual work before. Generally, registered users of p2p products are attracted by third-party channels. If the quality and quantity of traffic sources can be divided into four quadrants, then a fixed time point is selected to compare the traffic cost-effectiveness of each channel. The quality can be measured by the total amount of retention. as standard. Continue to maintain high quality and high quantity channels, expand the introduction quantity of high quality and low quantity channels, pass low quality and low quantity, and try the delivery strategies and requirements of low quality and high quantity. Such quadrant analysis allows us to conduct comparative analysis. You get a very intuitive and quick result.

3. Comparative analysis

Horizontal comparison: Horizontal comparison is to compare with yourself. The most common data indicators need to be compared with the target value to answer whether we have achieved the goal; compared with our last month, to answer how much we have grown around the north.

Vertical comparison: To put it simply, it means comparing with others. We need to compare with our competitors to answer our question about our share and position in the market.

Many people may say that comparative analysis sounds very simple. Let me give you an example. There is an e-commerce check-in page. Yesterday its pv was 5000. How do you feel when you hear such data?

You won’t feel anything. If the average PV of this check-in page is 10,000, it means there was a major problem yesterday. If the average PV of the check-in page is 2,000, it means there was a jump yesterday. The data is only for comparison. , can produce meaning.

4. Cross-analysis

Comparative analysis includes both horizontal and vertical comparisons. If you want both horizontal and vertical comparisons, there is the cross analysis method. The cross analysis method is to cross-present data from multiple dimensions and perform combined analysis from multiple angles.

When analyzing app data, it is usually divided into ios and Android.

The main function of cross analysis is to segment data from multiple dimensions and discover the most relevant dimensions to explore the reasons for data changes.

Explanation:

Trends, comparisons, quadrants, and intersections include the most basic parts of data analysis. Whether it is data verification or data analysis, finding trends, making comparisons, dividing quadrants, and making subdivisions, only data can play its due role.

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